1. Object Recognition a Machine Translation Learning a Lexicon for a Fixed Image Vocabulary Miriam Miklofsky

2. Lexicons A vocabulary of terms used in a subject A specialized list of terms Devices that predict one representation given another representation

3. Dataset Aligned bitext Annotated images Images with regions Unknown which region of image goes with which word from text

4. EM

5. Clustering K means clustering Vector quantize the image region representation Kullback-Leibler divergence Relative entropy Measure of difference of two probability distributions over the same event space

6. Evaluation Auto annotate images Quantize regions Use lexicon to determine word Annotate image with word

7. Results - Annotation Base results 80 words of 371 word vocabulary could be predicted Retraining Similar results but some words with higher recall and precision

8. Results(cont.) Null probability Recall decreases Precision increases Clustering of like words Recall values of clusters higher than for single words

9. Results -Correspondence Base results Some good words up to 70% correct prediction Null prediction Predict good words with greater probability Word clustering Prediction rate generally increases

10. Evaluation Human evaluation Images viewed by hand Somewhat subjective

12. EM (cont.)

13. KL Divergence